Overview

Dataset statistics

Number of variables12
Number of observations14999
Missing cells700
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory191.0 B

Variable types

Categorical2
Numeric10

Alerts

activity_days is highly overall correlated with driving_daysHigh correlation
driven_km_drives is highly overall correlated with duration_minutes_drivesHigh correlation
drives is highly overall correlated with sessions and 1 other fieldsHigh correlation
driving_days is highly overall correlated with activity_daysHigh correlation
duration_minutes_drives is highly overall correlated with driven_km_drivesHigh correlation
sessions is highly overall correlated with drives and 1 other fieldsHigh correlation
total_sessions is highly overall correlated with drives and 1 other fieldsHigh correlation
label has 700 (4.7%) missing valuesMissing
total_sessions has unique valuesUnique
driven_km_drives has unique valuesUnique
duration_minutes_drives has unique valuesUnique
total_navigations_fav1 has 3113 (20.8%) zerosZeros
total_navigations_fav2 has 6105 (40.7%) zerosZeros
activity_days has 243 (1.6%) zerosZeros
driving_days has 1024 (6.8%) zerosZeros

Reproduction

Analysis started2026-02-08 20:23:10.165556
Analysis finished2026-02-08 20:23:20.973060
Duration10.81 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

label
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing700
Missing (%)4.7%
Memory size831.9 KiB
retained
11763 
churned
2536 

Length

Max length8
Median length8
Mean length7.8226449
Min length7

Characters and Unicode

Total characters111856
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowretained
2nd rowretained
3rd rowretained
4th rowretained
5th rowretained

Common Values

ValueCountFrequency (%)
retained11763
78.4%
churned2536
 
16.9%
(Missing)700
 
4.7%

Length

2026-02-08T17:23:21.043384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-08T17:23:21.107897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
retained11763
82.3%
churned2536
 
17.7%

Most occurring characters

ValueCountFrequency (%)
e26062
23.3%
r14299
12.8%
n14299
12.8%
d14299
12.8%
a11763
10.5%
t11763
10.5%
i11763
10.5%
c2536
 
2.3%
h2536
 
2.3%
u2536
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)111856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e26062
23.3%
r14299
12.8%
n14299
12.8%
d14299
12.8%
a11763
10.5%
t11763
10.5%
i11763
10.5%
c2536
 
2.3%
h2536
 
2.3%
u2536
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)111856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e26062
23.3%
r14299
12.8%
n14299
12.8%
d14299
12.8%
a11763
10.5%
t11763
10.5%
i11763
10.5%
c2536
 
2.3%
h2536
 
2.3%
u2536
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)111856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e26062
23.3%
r14299
12.8%
n14299
12.8%
d14299
12.8%
a11763
10.5%
t11763
10.5%
i11763
10.5%
c2536
 
2.3%
h2536
 
2.3%
u2536
 
2.3%

sessions
Real number (ℝ)

High correlation 

Distinct469
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.633776
Minimum0
Maximum743
Zeros105
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2026-02-08T17:23:21.187732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q123
median56
Q3112
95-th percentile243
Maximum743
Range743
Interquartile range (IQR)89

Descriptive statistics

Standard deviation80.699065
Coefficient of variation (CV)1.0008097
Kurtosis5.9183196
Mean80.633776
Median Absolute Deviation (MAD)39
Skewness2.0100244
Sum1209426
Variance6512.3391
MonotonicityNot monotonic
2026-02-08T17:23:21.295619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4191
 
1.3%
3186
 
1.2%
8186
 
1.2%
2179
 
1.2%
13175
 
1.2%
7172
 
1.1%
9170
 
1.1%
5168
 
1.1%
6167
 
1.1%
20162
 
1.1%
Other values (459)13243
88.3%
ValueCountFrequency (%)
0105
0.7%
1162
1.1%
2179
1.2%
3186
1.2%
4191
1.3%
5168
1.1%
6167
1.1%
7172
1.1%
8186
1.2%
9170
1.1%
ValueCountFrequency (%)
7431
< 0.1%
7251
< 0.1%
6931
< 0.1%
6901
< 0.1%
6712
< 0.1%
6571
< 0.1%
6271
< 0.1%
6251
< 0.1%
6081
< 0.1%
6071
< 0.1%

drives
Real number (ℝ)

High correlation 

Distinct401
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.281152
Minimum0
Maximum596
Zeros106
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2026-02-08T17:23:21.409547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q120
median48
Q393
95-th percentile201
Maximum596
Range596
Interquartile range (IQR)73

Descriptive statistics

Standard deviation65.913872
Coefficient of variation (CV)0.97967812
Kurtosis5.6551845
Mean67.281152
Median Absolute Deviation (MAD)32
Skewness1.9707988
Sum1009150
Variance4344.6386
MonotonicityNot monotonic
2026-02-08T17:23:21.516161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7212
 
1.4%
4211
 
1.4%
8203
 
1.4%
3201
 
1.3%
2198
 
1.3%
10194
 
1.3%
11191
 
1.3%
16184
 
1.2%
17184
 
1.2%
5183
 
1.2%
Other values (391)13038
86.9%
ValueCountFrequency (%)
0106
0.7%
1172
1.1%
2198
1.3%
3201
1.3%
4211
1.4%
5183
1.2%
6178
1.2%
7212
1.4%
8203
1.4%
9169
1.1%
ValueCountFrequency (%)
5961
< 0.1%
5821
< 0.1%
5631
< 0.1%
5521
< 0.1%
5461
< 0.1%
5381
< 0.1%
5291
< 0.1%
5141
< 0.1%
5061
< 0.1%
5011
< 0.1%

total_sessions
Real number (ℝ)

High correlation  Unique 

Distinct14999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.96445
Minimum0.22021094
Maximum1216.1546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2026-02-08T17:23:21.621102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.22021094
5-th percentile34.011886
Q190.661156
median159.56811
Q3254.19234
95-th percentile454.3632
Maximum1216.1546
Range1215.9344
Interquartile range (IQR)163.53118

Descriptive statistics

Standard deviation136.40513
Coefficient of variation (CV)0.71805609
Kurtosis3.3355973
Mean189.96445
Median Absolute Deviation (MAD)77.738681
Skewness1.4915302
Sum2849276.7
Variance18606.359
MonotonicityNot monotonic
2026-02-08T17:23:21.731565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
353.41979711
 
< 0.1%
296.74827291
 
< 0.1%
326.89659621
 
< 0.1%
135.52292631
 
< 0.1%
67.589221271
 
< 0.1%
168.24702011
 
< 0.1%
279.54443731
 
< 0.1%
46.47161141
 
< 0.1%
53.736829711
 
< 0.1%
175.14984871
 
< 0.1%
Other values (14989)14989
99.9%
ValueCountFrequency (%)
0.22021094381
< 0.1%
0.65332273911
< 0.1%
1.2006597431
< 0.1%
1.3621288271
< 0.1%
2.8948517851
< 0.1%
3.1102133831
< 0.1%
3.2064529711
< 0.1%
3.2747850931
< 0.1%
3.3928763021
< 0.1%
3.4875899361
< 0.1%
ValueCountFrequency (%)
1216.1546331
< 0.1%
1155.9933151
< 0.1%
1117.8938211
< 0.1%
1076.8797411
< 0.1%
1051.8837331
< 0.1%
996.15071661
< 0.1%
983.29191431
< 0.1%
962.40150571
< 0.1%
945.79108561
< 0.1%
933.8925231
< 0.1%

n_days_after_onboarding
Real number (ℝ)

Distinct3441
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1749.8378
Minimum4
Maximum3500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2026-02-08T17:23:21.839428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile175.9
Q1878
median1741
Q32623.5
95-th percentile3314
Maximum3500
Range3496
Interquartile range (IQR)1745.5

Descriptive statistics

Standard deviation1008.5139
Coefficient of variation (CV)0.57634707
Kurtosis-1.2006952
Mean1749.8378
Median Absolute Deviation (MAD)872
Skewness0.0019725005
Sum26245817
Variance1017100.2
MonotonicityNot monotonic
2026-02-08T17:23:21.984968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2713
 
0.1%
308413
 
0.1%
69213
 
0.1%
323513
 
0.1%
190112
 
0.1%
212712
 
0.1%
64811
 
0.1%
241211
 
0.1%
212211
 
0.1%
140411
 
0.1%
Other values (3431)14879
99.2%
ValueCountFrequency (%)
41
 
< 0.1%
61
 
< 0.1%
72
 
< 0.1%
83
< 0.1%
92
 
< 0.1%
104
< 0.1%
112
 
< 0.1%
133
< 0.1%
141
 
< 0.1%
157
< 0.1%
ValueCountFrequency (%)
35004
< 0.1%
34987
< 0.1%
34973
< 0.1%
34966
< 0.1%
34956
< 0.1%
34945
< 0.1%
34932
 
< 0.1%
34923
< 0.1%
34915
< 0.1%
34903
< 0.1%

total_navigations_fav1
Real number (ℝ)

Zeros 

Distinct730
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.60597
Minimum0
Maximum1236
Zeros3113
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2026-02-08T17:23:22.102139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median71
Q3178
95-th percentile424
Maximum1236
Range1236
Interquartile range (IQR)169

Descriptive statistics

Standard deviation148.12154
Coefficient of variation (CV)1.218045
Kurtosis5.2030052
Mean121.60597
Median Absolute Deviation (MAD)71
Skewness1.9766807
Sum1823968
Variance21939.992
MonotonicityNot monotonic
2026-02-08T17:23:22.214216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03113
 
20.8%
2791
 
0.6%
684
 
0.6%
1382
 
0.5%
1680
 
0.5%
279
 
0.5%
378
 
0.5%
777
 
0.5%
1877
 
0.5%
1976
 
0.5%
Other values (720)11162
74.4%
ValueCountFrequency (%)
03113
20.8%
174
 
0.5%
279
 
0.5%
378
 
0.5%
471
 
0.5%
575
 
0.5%
684
 
0.6%
777
 
0.5%
856
 
0.4%
969
 
0.5%
ValueCountFrequency (%)
12361
< 0.1%
11821
< 0.1%
11701
< 0.1%
11601
< 0.1%
11581
< 0.1%
11531
< 0.1%
11041
< 0.1%
10961
< 0.1%
10851
< 0.1%
10771
< 0.1%

total_navigations_fav2
Real number (ℝ)

Zeros 

Distinct287
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.672512
Minimum0
Maximum415
Zeros6105
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2026-02-08T17:23:22.334860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q343
95-th percentile124
Maximum415
Range415
Interquartile range (IQR)43

Descriptive statistics

Standard deviation45.394651
Coefficient of variation (CV)1.5298554
Kurtosis7.8213232
Mean29.672512
Median Absolute Deviation (MAD)9
Skewness2.4181758
Sum445058
Variance2060.6743
MonotonicityNot monotonic
2026-02-08T17:23:22.466639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06105
40.7%
2197
 
1.3%
4175
 
1.2%
1166
 
1.1%
3165
 
1.1%
5163
 
1.1%
7159
 
1.1%
9157
 
1.0%
6154
 
1.0%
11153
 
1.0%
Other values (277)7405
49.4%
ValueCountFrequency (%)
06105
40.7%
1166
 
1.1%
2197
 
1.3%
3165
 
1.1%
4175
 
1.2%
5163
 
1.1%
6154
 
1.0%
7159
 
1.1%
8134
 
0.9%
9157
 
1.0%
ValueCountFrequency (%)
4151
< 0.1%
3961
< 0.1%
3941
< 0.1%
3751
< 0.1%
3581
< 0.1%
3561
< 0.1%
3551
< 0.1%
3541
< 0.1%
3521
< 0.1%
3441
< 0.1%

driven_km_drives
Real number (ℝ)

High correlation  Unique 

Distinct14999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4039.3409
Minimum60.44125
Maximum21183.402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2026-02-08T17:23:22.580328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60.44125
5-th percentile1054.4338
Q12212.6006
median3493.8581
Q35289.8613
95-th percentile8889.7942
Maximum21183.402
Range21122.961
Interquartile range (IQR)3077.2607

Descriptive statistics

Standard deviation2502.1493
Coefficient of variation (CV)0.61944495
Kurtosis2.3511147
Mean4039.3409
Median Absolute Deviation (MAD)1451.3832
Skewness1.3012261
Sum60586074
Variance6260751.3
MonotonicityNot monotonic
2026-02-08T17:23:22.686105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6030.4987731
 
< 0.1%
2628.8450681
 
< 0.1%
13715.920551
 
< 0.1%
3059.1488181
 
< 0.1%
913.59112311
 
< 0.1%
3950.2020081
 
< 0.1%
901.23869851
 
< 0.1%
8092.4580821
 
< 0.1%
3134.0007481
 
< 0.1%
2593.6300891
 
< 0.1%
Other values (14989)14989
99.9%
ValueCountFrequency (%)
60.441250461
< 0.1%
159.44405461
< 0.1%
167.50531991
< 0.1%
178.23231331
< 0.1%
179.56199611
< 0.1%
190.03029161
< 0.1%
195.99653461
< 0.1%
199.58708521
< 0.1%
205.61186121
< 0.1%
208.77036551
< 0.1%
ValueCountFrequency (%)
21183.401891
< 0.1%
20108.364121
< 0.1%
19214.475111
< 0.1%
19157.588381
< 0.1%
18129.927711
< 0.1%
18127.047151
< 0.1%
17921.816061
< 0.1%
17611.932161
< 0.1%
16611.739021
< 0.1%
16480.939081
< 0.1%

duration_minutes_drives
Real number (ℝ)

High correlation  Unique 

Distinct14999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1860.976
Minimum18.282082
Maximum15851.727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2026-02-08T17:23:22.793681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.282082
5-th percentile319.39336
Q1835.99626
median1478.2499
Q32464.3626
95-th percentile4668.8993
Maximum15851.727
Range15833.445
Interquartile range (IQR)1628.3664

Descriptive statistics

Standard deviation1446.7023
Coefficient of variation (CV)0.777389
Kurtosis4.7214293
Mean1860.976
Median Absolute Deviation (MAD)751.70161
Skewness1.7660189
Sum27912779
Variance2092947.5
MonotonicityNot monotonic
2026-02-08T17:23:22.911579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3042.4364231
 
< 0.1%
1985.7750611
 
< 0.1%
3160.4729141
 
< 0.1%
1610.7359041
 
< 0.1%
587.19654231
 
< 0.1%
1219.5559241
 
< 0.1%
439.10139731
 
< 0.1%
5303.6788231
 
< 0.1%
753.58860231
 
< 0.1%
1581.0931361
 
< 0.1%
Other values (14989)14989
99.9%
ValueCountFrequency (%)
18.282082471
< 0.1%
23.022684791
< 0.1%
23.222707071
< 0.1%
24.466717451
< 0.1%
29.498317381
< 0.1%
32.229273411
< 0.1%
33.911350741
< 0.1%
34.307481471
< 0.1%
35.033705691
< 0.1%
35.736867781
< 0.1%
ValueCountFrequency (%)
15851.727161
< 0.1%
11328.678781
< 0.1%
11230.893321
< 0.1%
11228.804541
< 0.1%
10972.762171
< 0.1%
10796.534361
< 0.1%
10726.968111
< 0.1%
10348.408781
< 0.1%
10340.794411
< 0.1%
10318.974141
< 0.1%

activity_days
Real number (ℝ)

High correlation  Zeros 

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.537102
Minimum0
Maximum31
Zeros243
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2026-02-08T17:23:23.007510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median16
Q323
95-th percentile29
Maximum31
Range31
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0046554
Coefficient of variation (CV)0.57955821
Kurtosis-1.2032636
Mean15.537102
Median Absolute Deviation (MAD)8
Skewness-0.0077143675
Sum233041
Variance81.083818
MonotonicityNot monotonic
2026-02-08T17:23:23.097020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
17546
 
3.6%
21525
 
3.5%
29525
 
3.5%
28522
 
3.5%
1510
 
3.4%
5506
 
3.4%
24499
 
3.3%
7493
 
3.3%
10492
 
3.3%
2491
 
3.3%
Other values (22)9890
65.9%
ValueCountFrequency (%)
0243
1.6%
1510
3.4%
2491
3.3%
3474
3.2%
4461
3.1%
5506
3.4%
6483
3.2%
7493
3.3%
8486
3.2%
9458
3.1%
ValueCountFrequency (%)
31246
1.6%
30479
3.2%
29525
3.5%
28522
3.5%
27460
3.1%
26473
3.2%
25478
3.2%
24499
3.3%
23456
3.0%
22469
3.1%

driving_days
Real number (ℝ)

High correlation  Zeros 

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.179879
Minimum0
Maximum30
Zeros1024
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2026-02-08T17:23:23.193415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median12
Q319
95-th percentile25
Maximum30
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.8240361
Coefficient of variation (CV)0.6423739
Kurtosis-1.0807976
Mean12.179879
Median Absolute Deviation (MAD)7
Skewness0.097109286
Sum182686
Variance61.215541
MonotonicityNot monotonic
2026-02-08T17:23:23.302050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
01024
 
6.8%
17615
 
4.1%
19608
 
4.1%
18601
 
4.0%
16598
 
4.0%
10596
 
4.0%
3583
 
3.9%
14580
 
3.9%
2577
 
3.8%
11577
 
3.8%
Other values (21)8640
57.6%
ValueCountFrequency (%)
01024
6.8%
1548
3.7%
2577
3.8%
3583
3.9%
4495
3.3%
5561
3.7%
6570
3.8%
7534
3.6%
8574
3.8%
9555
3.7%
ValueCountFrequency (%)
3012
 
0.1%
2948
 
0.3%
28111
 
0.7%
27169
 
1.1%
26229
1.5%
25290
1.9%
24365
2.4%
23407
2.7%
22484
3.2%
21508
3.4%

device
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size810.9 KiB
iPhone
9672 
Android
5327 

Length

Max length7
Median length6
Mean length6.355157
Min length6

Characters and Unicode

Total characters95321
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAndroid
2nd rowiPhone
3rd rowAndroid
4th rowiPhone
5th rowAndroid

Common Values

ValueCountFrequency (%)
iPhone9672
64.5%
Android5327
35.5%

Length

2026-02-08T17:23:23.396582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-08T17:23:23.449120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
iphone9672
64.5%
android5327
35.5%

Most occurring characters

ValueCountFrequency (%)
i14999
15.7%
n14999
15.7%
o14999
15.7%
d10654
11.2%
h9672
10.1%
P9672
10.1%
e9672
10.1%
A5327
 
5.6%
r5327
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)95321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i14999
15.7%
n14999
15.7%
o14999
15.7%
d10654
11.2%
h9672
10.1%
P9672
10.1%
e9672
10.1%
A5327
 
5.6%
r5327
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)95321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i14999
15.7%
n14999
15.7%
o14999
15.7%
d10654
11.2%
h9672
10.1%
P9672
10.1%
e9672
10.1%
A5327
 
5.6%
r5327
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)95321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i14999
15.7%
n14999
15.7%
o14999
15.7%
d10654
11.2%
h9672
10.1%
P9672
10.1%
e9672
10.1%
A5327
 
5.6%
r5327
 
5.6%

Interactions

2026-02-08T17:23:19.771317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-08T17:23:13.909758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:14.881910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:15.862531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:16.788342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:17.745139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-08T17:23:18.562055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:19.599693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:20.592695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:11.831613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:12.846261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:13.813473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:14.777288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:15.769274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:16.700146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:17.649219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:18.655271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-08T17:23:19.683814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-08T17:23:23.504788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
activity_daysdevicedriven_km_drivesdrivesdriving_daysduration_minutes_driveslabeln_days_after_onboardingsessionstotal_navigations_fav1total_navigations_fav2total_sessions
activity_days1.0000.000-0.0090.0230.950-0.0080.313-0.0100.0240.013-0.0050.016
device0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
driven_km_drives-0.0090.0001.0000.005-0.0110.6810.024-0.0030.005-0.0090.0100.004
drives0.0230.0000.0051.0000.017-0.0010.0440.0010.9980.0090.0090.581
driving_days0.9500.000-0.0110.0171.000-0.0090.301-0.0080.0180.0120.0010.013
duration_minutes_drives-0.0080.0000.681-0.001-0.0091.0000.033-0.009-0.002-0.0010.0020.003
label0.3130.0000.0240.0440.3010.0331.0000.1280.0410.0570.0390.032
n_days_after_onboarding-0.0100.000-0.0030.001-0.008-0.0090.1281.0000.0010.002-0.0070.006
sessions0.0240.0000.0050.9980.018-0.0020.0410.0011.0000.0110.0090.583
total_navigations_fav10.0130.000-0.0090.0090.012-0.0010.0570.0020.0111.0000.0030.001
total_navigations_fav2-0.0050.0000.0100.0090.0010.0020.039-0.0070.0090.0031.0000.006
total_sessions0.0160.0000.0040.5810.0130.0030.0320.0060.5830.0010.0061.000

Missing values

2026-02-08T17:23:20.726097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-08T17:23:20.855411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

labelsessionsdrivestotal_sessionsn_days_after_onboardingtotal_navigations_fav1total_navigations_fav2driven_km_drivesduration_minutes_drivesactivity_daysdriving_daysdevice
0retained283226296.748273227620802628.8450681985.7750612819Android
1retained133107326.8965961225196413715.9205503160.4729141311iPhone
2retained11495135.5229262651003059.1488181610.735904148Android
3retained494067.589221153227913.591123587.19654273iPhone
4retained8468168.247020156216653950.2020081219.5559242718Android
5retained113103279.544437263700901.238699439.1013971511iPhone
6retained32236.725314360185185249.172828726.5772052823iPhone
7retained3935176.0728452999007892.0524682466.9817412220iPhone
8retained5746183.5320184240262651.7097641594.3429842520Android
9churned8468244.80211529977206043.4602952341.83852873iPhone
labelsessionsdrivestotal_sessionsn_days_after_onboardingtotal_navigations_fav1total_navigations_fav2driven_km_drivesduration_minutes_drivesactivity_daysdriving_daysdevice
14989retained66153.3736162988371241553.284152833.4211552320iPhone
14990churned7361329.90430061460466090.4501543323.88077100Android
14991churned5041102.444592146301214094.5363132201.98421042iPhone
14992retained11290267.04013612831401525.9321431116.65047855iPhone
14993NaN675797.57007411312071022267.052913318.1206342726iPhone
14994retained6055207.87562214031702890.4969012186.1557082517iPhone
14995retained4235187.670313250515104062.5751941208.5831932520Android
14996retained273219422.01724118731703097.8250281031.2787061817iPhone
14997churned149120180.52418431504504051.758549254.18776366iPhone
14998retained7358353.419797338313516030.4987733042.4364231413iPhone